Articles on trading system automation in MQL5

A monitoring of the current state of a trade account implies controlling open positions and orders. Before a trade signal becomes a deal, it should be sent from the client terminal as a request to the trade server, where it will be placed in the order queue awaiting to be processed. Accepting of a request by the trade server, deleting it as it expires or conducting a deal on its basis - all those actions are followed by trade events; and the trade server informs the terminal about them.

Many people love them but a few understand the whole operations behind Neural Networks. In this article I will try to explain everything that goes behind closed doors of a feed-forward multi-layer perception in plain English.

In this article, we will learn how to create a custom indicator using MQL5 based on our preferences, to be used in MetaTrader 5 to help us read charts or to be used in automated Expert Advisors.

In this article, we explored the classic grid strategy, detailing its automation using an Expert Advisor in MQL5 and analyzing initial backtest results. We highlighted the strategy's need for high holding capacity and outlined plans for optimizing key parameters like distance, takeProfit, and lot sizes in future installments. The series aims to enhance trading strategy efficiency and adaptability to different market conditions.

Imagine that you can use data that is not found in MetaTrader, you only get data from indicators by price analysis and technical analysis. Now imagine that you can access data that will take your trading power steps higher. You can multiply the power of the MetaTrader software if you mix the output of other software, macro analysis methods, and ultra-advanced tools through the ​​API data. In this article, we will teach you how to use APIs and introduce useful and valuable API data services.

The article provides the code and the description of the main stages of the machine learning process using a specific example. To obtain the model, you do not need Python or R knowledge. Furthermore, basic MQL5 knowledge is enough — this is exactly my level. Therefore, I hope that the article will serve as a good tutorial for a broad audience, assisting those interested in evaluating machine learning capabilities and in implementing them in their programs.

In this article, we build a grid trading expert advisor in MQL5 that uses dynamic lot scaling. We cover the strategy design, code implementation, and backtesting process. Finally, we share key insights and best practices for optimizing the automated trading system.

There are minor things to cover on the feed-forward neural network before we are through, the design being one of them. Let's see how we can build and design a flexible neural network to our inputs, the number of hidden layers, and the nodes for each of the network.

In this new article in our series, we will learn about a new technical tool that may be useful in our trading. It is the Awesome Oscillator (AO) indicator. We will learn how to design a trading system by this indicator.

This article explains how to design and optimise a trading system using the Detrended Price Oscillator (DPO) in MQL5. It outlines the indicator's core logic, demonstrating how it identifies short-term cycles by filtering out long-term trends. Through a series of step-by-step examples and simple strategies, readers will learn how to code it, define entry and exit signals, and conduct backtesting. Finally, the article presents practical optimization methods to enhance performance and adapt the system to changing market conditions.

A step-by-step guide to implementing an automated trading algorithm in MQL5 based on the Bollinger Bands trading strategy. A detailed tutorial based on creating an Expert Advisor that can be useful for traders.

As the next step in studying neural networks, I suggest considering the methods of increasing convergence during neural network training. There are several such methods. In this article we will consider one of them entitled Dropout.

I invite you to get acquainted with the Multi-Agent Self-Adaptive (MASA) framework, which combines reinforcement learning and adaptive strategies, providing a harmonious balance between profitability and risk management in turbulent market conditions.

This is one of the most powerful indicators currently existing. Anyone who trades trying to have a certain degree of confidence must have this indicator on their chart. Most often the indicator is used by those who prefer “tape reading” while trading. Also, this indicator can be utilized by those who use only Price Action while trading.

In this article, we develop the Adaptive Crossover RSI Trading Suite System, which uses 14- and 50-period moving average crossovers for signals, confirmed by a 14-period RSI filter. The system includes a trading day filter, signal arrows with annotations, and a real-time dashboard for monitoring. This approach ensures precision and adaptability in automated trading.

An inverse fair value gap(IFVG) occurs when price returns to a previously identified fair value gap and, instead of showing the expected supportive or resistive reaction, fails to respect it. This failure can signal a potential shift in market direction and offer a contrarian trading edge. In this article, I'm going to introduce my self-developed approach to quantifying and utilizing inverse fair value gap as a strategy for MetaTrader 5 expert advisors.

In this article, we build an MQL5 Expert Advisor to detect Butterfly harmonic patterns. We identify pivot points and validate Fibonacci levels to confirm the pattern. We then visualize the pattern on the chart and automatically execute trades when confirmed.

Welcome to a new article from our series about learning how to design a trading system by the most popular technical indicators, in this article we will learn about a new technical tool and learn how to design a trading system by Variable Index Dynamic Average (VIDYA).

In this article, we develop a London Session Breakout System that identifies pre-London range breakouts and places pending orders with customizable trade types and risk settings. We incorporate features like trailing stops, risk-to-reward ratios, maximum drawdown limits, and a control panel for real-time monitoring and management.

Training the CatBoost classifier in Python and exporting the model to mql5, as well as parsing the model parameters and a custom strategy tester. The Python language and the MetaTrader 5 library are used for preparing the data and for training the model.

In this article, we will continue our series about how to design a trading system using the most popular indicators. In this article, we will learn about the Parabolic SAR indicator in detail and how we can design a trading system to be used in MetaTrader 5 using some simple strategies.

Join us in Part III of the "Improve Your Trading Charts With Interactive GUIs in MQL5" series as we explore the integration of interactive GUIs into movable trading dashboards in MQL5. This article builds on the foundations set in Parts I and II, guiding readers to transform static trading dashboards into dynamic, movable ones.

In this article, we develop a trendline trader program that uses least squares fit to detect support and resistance trendlines, generating dynamic buy and sell signals based on price touches and open positions based on generated signals.

A new article in our series about how to design a trading system by the most popular technical indicator. In this article, we will learn how to do that by the Relative Vigor Index indicator.

The Multi-Currency Expert Advisor in this article is Expert Advisor or trading robot that can trade (open orders, close orders and manage orders an more) for more than 1 symbol pair only from one symbol chart.